Spatial-temporal medical images such as those obtained from Cardiac Magnetic Resonance (CMR) images provide important information for diagnosis and treatment of cardiovascular diseases in a non-invasive manner. The CMR technique is considered the current gold standard for imaging the structure and function of the heart. To help in the diagnosis of disease, physicians are interested in identifying the heart chambers and measuring the change in ventricular blood volume (i.e., ejection fraction=stroke volume/end-diastolic volume×100%) and wall thickening over the cardiac cycle. Accurate extraction of the anatomical boundaries of the heart chambers is crucial to obtain reproducible quantitative measurements to support the diagnosis and follow-up of cardiac pathologies. However, manual delineation of the anatomical structures is time-consuming and tedious (even a trained clinician takes 20 minutes to do this), and limited by inter- and intra-observer variability. Therefore, it is highly desirable to develop techniques for automatic CMR image segmentation (that is, the identification of contours within images which, correspond to the outlines of specified anatomical structures).
Automatic segmentation of the human left ventricle (LV) and right ventricle (RV) from CMR data, in particular LV segmentation and tracking, has been addressed in the last two decades. Known ventricle segmentation techniques can be broadly classified into four major approaches: image-based methods, deformable model-based methods, registration-based-methods, and graph-based methods. The first approach utilizes basic image analysis operators such as thresholding, region-growing, image morphology, edge detection, pixel classification, etc, to delineate the LV and RV boundaries from the image. It uses information obtained directly from the image itself and requires no or minimal prior information from the user (i.e. the human operator of the computer). The second approach trains a shape/curve model of the LV or RV using images of previous subjects, and lets the curve model evolve in images of new subjects until it converges to the LV or RV boundaries. It takes advantage of the fact that ventricles of different subjects have similar shapes. The basic idea of the third approach is to transfer expert segmentations of training images (i.e., atlases) onto target images through image registration, and then fuse the transferred segmentations to derive an ultimate segmentation. The last approach also does not require heavy reliance on explicitly learned or encoded priors, but the user has to initialize a set of foreground and background seeds.
Although ventricle segmentation methods have improved over the last few decades, accurate LV and RV segmentation is still acknowledged as a difficult problem, especially for RV segmentation (due to the high anatomical complexity and poorly-delineated ventricular boundaries of the RV). Clinical applicability of most of the developed techniques for segmenting cardiac structures robustly is yet to be realised. Automatic segmentation of LV and RV from CMR data typically faces four challenges: 1) the lack of edge information; 2) the overlap between the intensity distributions within the cardiac regions; 3) the shape variability of the ventricle contours across slices and phases; and 4) the inter-subject variability of these factors.
Conventionally, the most common technique to handle these challenges is to incorporate prior model information into the segmentation, such as the active shape model, active appearance model, and anatomical atlas registration model, etc. However, such models need to be constructed or learned from many manually segmented images, which is cumbersome, labour-intensive, subjective and of limited use due to anatomical variability (pathology typically causes large variability in anatomical structures) and image contrast variability (e.g., due to artefacts or different imaging protocols). In addition, most existing ventricle segmentation methods operate on static images. As a result, the segmentation performance is limited by the data available in an individual frame, particularly for low signal-to-noise ratio (SNR) images, where the observation from a single frame alone may not provide enough information for a good segmentation.
Furthermore, to achieve full automation and eliminate inter- and intra-observer variability, the initialization of an image segmentation algorithm should also be automatic, and there is currently still a need for a fast and robust initialization procedure. Many automatic medical image segmentation techniques rely on a combination of information directly derived from the image and information provided by prior models of anatomy and its appearance in the image. Due to the limitations and the construction cost of prior models, methods that rely primarily on image information have distinct advantages.